Dynamic Capacity Routing

Defines a multidimensional model-selection and routing mechanism based on static and dynamic Capacity Vectors.

1. Purpose

Hardcoding a specific model name is brittle against model evolution and availability changes. AIKernel abstracts models as bundles of reasoning capability and compute characteristics, and provides:

• Model-agnostic abstraction: define required capabilities (logic, creativity, latency, rigidity) and dynamically resolve best-fit models.
• Runtime-context adaptation: select execution lanes using dynamic constraints such as budget state, hardware load, and latency conditions.
• Cost/quality optimization: automatically route routine tasks to lighter models when high-cost models are unnecessary.

2. Capacity Vector Model

AIKernel treats model capability as coordinates in a multidimensional vector space.

2.1 Static Capacity

Relatively stable, model-intrinsic indicators.

• Logic_Depth: reasoning, coding, and mathematical depth.
• Creative_Fluency: expressive diversity and language naturalness.
• Constraint_Rigidity: compliance with system constraints and structured output formats.
• Knowledge_Cutoff: recency of learned knowledge.

2.2 Dynamic Capacity

Indicators that vary by runtime and contract conditions.

• Token_Economic_Efficiency: cost fitness against remaining budget.
• Latency_Score: current response speed and throughput stability.
• Hardware_Locality: suitability for edge vs cloud execution locality.

3. Core Contracts

3.1 ModelCapacityVector and ICapacityAxis

• Responsibility: immutable container for static capability values.
• Definition: each axis is normalized to 0.0..1.0 for comparable scoring.

3.2 IDynamicCapacityProvider

• Responsibility: observe runtime conditions (rate limits, budget burn, system load) and supply IDynamicCapacityVector.

3.3 IVectorMatcher

• Responsibility: compare required vectors (task needs) against candidate vectors (providers/models) and compute fit scores.
• Note: supports weighted scoring and hard-gate filters, not only naive distance.

3.4 IModelVectorRouter

• Responsibility: choose the final IModelProvider from matcher results and establish the execution path.

4. Routing Rules

1. Intent-based requirement build: before execution, IKernel derives minimum logic depth, maximum cost, and other constraints from task metadata.
2. Dynamic override: when security constraints or severe budget pressure occur, dynamic axes may override static capability preference.
3. Fallback strategy: if the best-fit model is unavailable (for example rate limited), IModelVectorRouter retries with the next viable candidate or degrades gracefully.

5. Fail-Closed Rule

AIKernel does not continue with underqualified models.

• Requirement miss stop: if no candidate meets mandatory-axis thresholds, execution is denied.
• Explicit routing failure: return which axes were insufficient (for example required Logic_Depth >= 0.8, best available 0.6).
• Budget hard-stop: when IDynamicCapacityProvider detects budget hard limit, all downstream routing is blocked.

6. Mathematical Concept (Intuition)

$$Score = \sum_{i \in Axes} (Requirement_i \times Capability_i \times Weight_i)$$

Note: in practice, AIKernel combines linear scoring with blocking filters that force score to zero when critical axes (for example Security_Level) are below required thresholds.

Changelog

• v0.0.0 / v0.0.0.0: Initial draft
• v0.0.1 (2026-05-06): Version upgrade aligned with documentation guidelines